Big Data Analytics with Python Training Course
Gain the skills you need to advance your career. This course offers hands-on learning, expert guidance, and real-world applications designed to help you grow.
Course Overview
| Course Title | Big Data Analytics with Python Training Course |
| Organization | Tech For Development (T4D) |
| Venue | Tech For Development (T4D) Training Center along Tala Road, Runda, Nairobi |
| Duration | 10 days |
| Target Industries |
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| Target Job Roles |
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| Course Fees (Face-to-Face) | USD 2,200/KES 150,000 (Exclusive of VAT) |
| Course Fees (Virtual) | |
| Training Modes | Virtual and face-to-face training |
| Payment | Payment should be made to the Tech For Development (T4D) bank account on or before the start of the course |
| Accreditation | Tech For Development Certificate of Course Completion |
Course Overview
This intensive course offered by T4D is designed to equip participants with the skills and knowledge needed to leverage Python for processing, analyzing, and visualizing large datasets. The course covers essential Python libraries and frameworks used in big data analytics, including pandas, matplotlib, seaborn, and PySpark. Participants will gain hands-on experience through practical projects and case studies, enabling them to apply their learning to real-world data challenges.
Duration
10 Days
Target Audience
- Data scientists
- Data analysts
- Python developers
- IT professionals
- Business analysts
Personal Impact
- Enhance proficiency in Python for big data analytics.
- Develop skills for data manipulation, analysis, and visualization.
- Gain hands-on experience with real-world big data projects.
- Improve ability to derive insights from large datasets.
Organizational Impact
- Promote advanced data analytics practices within the organization.
- Enhance decision-making through data-driven insights.
- Ensure effective utilization of big data technologies.
- Improve overall data management and analytics capabilities.
Course Objectives
- Understand the fundamentals of big data analytics with Python.
- Learn data manipulation techniques using pandas.
- Develop skills for data visualization with matplotlib and seaborn.
- Gain proficiency in big data processing with PySpark.
- Explore machine learning applications with Python.
- Understand best practices for handling large datasets.
- Learn to implement scalable data analytics solutions.
- Gain insights into practical applications of big data analytics.
- Develop skills for working with unstructured data.
- Enhance ability to communicate data insights effectively.
Course Modules
Course Outline
Module 1: Introduction to Big Data Analytics
- Overview of big data analytics
- Importance of Python in big data
- Key concepts and definitions
- Case Study: Analyzing Customer Behavior Using Big Data
Module 2: Python Basics for Data Analytics
- Python programming fundamentals
- Data structures and libraries
- Setting up the Python environment
- Practical Component: Developing a Simple Data Analytics Application Using Python
Module 3: Data Manipulation with pandas
- Introduction to pandas
- Data cleaning and preparation
- Advanced data manipulation techniques
- Practical Component: Cleaning and Analyzing a Real-World Dataset Using pandas
Module 4: Data Visualization with matplotlib and seaborn
- Basics of data visualization
- Creating plots with matplotlib
- Enhancing visualizations with seaborn
- Practical Component: Visualizing Sales Data to Identify Trends and Patterns
Module 5: Introduction to PySpark
- Overview of PySpark
- Setting up PySpark environment
- Working with RDDs and DataFrames
- Case Study: Using PySpark for Large-Scale Data Processing in a Retail Environment
Module 6: Big Data Processing with PySpark
- Data processing workflows in PySpark
- Transformations and actions
- Performance optimization
- Practical Component: Optimizing Data Processing Workflows for a Social Media Platform Using PySpark
Module 7: Machine Learning with Python
- Introduction to machine learning
- Using scikit-learn for machine learning
- Implementing machine learning models
- Practical Component: Building a Predictive Model for Customer Churn Using Python
Module 8: Handling Unstructured Data
- Understanding unstructured data
- Techniques for processing unstructured data
- Case Study: Text Mining for Sentiment Analysis in Customer Reviews
Module 9: Practical Projects in Big Data Analytics
- Real-world big data projects
- Applying learned skills to projects
- Project presentations and feedback
- Practical Component: Capstone Project - Building a Big Data Solution for a Specific Industry Problem
Module 10: Communicating Data Insights
- Best practices for data storytelling
- Creating impactful data reports
- Effective communication of data insights
- Practical Component: Presenting Data-Driven Insights to Stakeholders in a Business Scenario
Course Administration
Methodology
Instructor-led sessions use a blended learning approach combining presentations, guided practical exercises, web-based tutorials, and group work, delivered by seasoned industry experts. All facilitation and course materials are in English, so participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued a Tech For Development Certificate of Course Completion certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at the Tech For Development Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation & Airport Transfer
Arranged upon request.For reservations contact the Training Officer:
Email: [email protected]
Phone: (+254) 790 824 179
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our T4D Training Centre or at a convenient location.
Payment
Payment should be transferred to the T4D account through a bank on or before the start of the course. Send proof of payment to [email protected].
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